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Classification of social anhedonia using temporal and spatial network features from a social cognition fMRI task

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@article{081095712eb449c892b775cb85068808,
title = "Classification of social anhedonia using temporal and spatial network features from a social cognition fMRI task",
abstract = "Previous studies have suggested that the degree of social anhedonia reflects the vulnerability for developing schizophrenia. However, only few studies have investigated how functional network changes are related to social anhedonia. The aim of this fMRI study was to classify subjects according to their degree of social anhedonia using supervised machine learning. More specifically, we extracted both spatial and temporal network features during a social cognition task from 70 subjects, and used support vector machines for classification. Since impairment in social cognition is well established in schizophrenia-spectrum disorders, the subjects performed a comic strip task designed to specifically probe theory of mind (ToM) and empathy processing. Features representing both temporal (time series) and network dynamics were extracted using task activation maps, seed region analysis, independent component analysis (ICA), and a newly developed multi-subject archetypal analysis (MSAA), which here aimed to further bridge aspects of both seed region analysis and decomposition by incorporating a spotlight approach.We found significant classification of subjects with elevated levels of social anhedonia when using the times series extracted using MSAA, indicating that temporal dynamics carry important information for classification of social anhedonia. Interestingly, we found that the same time series yielded the highest classification performance in a task classification of the ToM condition. Finally, the spatial network corresponding to that time series included both prefrontal and temporal-parietal regions as well as insula activity, which previously have been related schizotypy and the development of schizophrenia.",
keywords = "archetypical analysis, decomposition, functional connectivity, social anhedonia, support vector classification",
author = "Krohne, {Laerke Gebser} and Yi Wang and Hinrich, {Jesper L} and Morten Moerup and Chan, {Raymond C K} and Madsen, {Kristoffer H}",
note = "{\circledC} 2019 Wiley Periodicals, Inc.",
year = "2019",
month = "12",
day = "1",
doi = "10.1002/hbm.24751",
language = "English",
volume = "40",
pages = "4965--4981",
journal = "Human Brain Mapping",
issn = "1065-9471",
publisher = "John/Wiley & Sons, Inc. John/Wiley & Sons Ltd",
number = "17",

}

RIS

TY - JOUR

T1 - Classification of social anhedonia using temporal and spatial network features from a social cognition fMRI task

AU - Krohne, Laerke Gebser

AU - Wang, Yi

AU - Hinrich, Jesper L

AU - Moerup, Morten

AU - Chan, Raymond C K

AU - Madsen, Kristoffer H

N1 - © 2019 Wiley Periodicals, Inc.

PY - 2019/12/1

Y1 - 2019/12/1

N2 - Previous studies have suggested that the degree of social anhedonia reflects the vulnerability for developing schizophrenia. However, only few studies have investigated how functional network changes are related to social anhedonia. The aim of this fMRI study was to classify subjects according to their degree of social anhedonia using supervised machine learning. More specifically, we extracted both spatial and temporal network features during a social cognition task from 70 subjects, and used support vector machines for classification. Since impairment in social cognition is well established in schizophrenia-spectrum disorders, the subjects performed a comic strip task designed to specifically probe theory of mind (ToM) and empathy processing. Features representing both temporal (time series) and network dynamics were extracted using task activation maps, seed region analysis, independent component analysis (ICA), and a newly developed multi-subject archetypal analysis (MSAA), which here aimed to further bridge aspects of both seed region analysis and decomposition by incorporating a spotlight approach.We found significant classification of subjects with elevated levels of social anhedonia when using the times series extracted using MSAA, indicating that temporal dynamics carry important information for classification of social anhedonia. Interestingly, we found that the same time series yielded the highest classification performance in a task classification of the ToM condition. Finally, the spatial network corresponding to that time series included both prefrontal and temporal-parietal regions as well as insula activity, which previously have been related schizotypy and the development of schizophrenia.

AB - Previous studies have suggested that the degree of social anhedonia reflects the vulnerability for developing schizophrenia. However, only few studies have investigated how functional network changes are related to social anhedonia. The aim of this fMRI study was to classify subjects according to their degree of social anhedonia using supervised machine learning. More specifically, we extracted both spatial and temporal network features during a social cognition task from 70 subjects, and used support vector machines for classification. Since impairment in social cognition is well established in schizophrenia-spectrum disorders, the subjects performed a comic strip task designed to specifically probe theory of mind (ToM) and empathy processing. Features representing both temporal (time series) and network dynamics were extracted using task activation maps, seed region analysis, independent component analysis (ICA), and a newly developed multi-subject archetypal analysis (MSAA), which here aimed to further bridge aspects of both seed region analysis and decomposition by incorporating a spotlight approach.We found significant classification of subjects with elevated levels of social anhedonia when using the times series extracted using MSAA, indicating that temporal dynamics carry important information for classification of social anhedonia. Interestingly, we found that the same time series yielded the highest classification performance in a task classification of the ToM condition. Finally, the spatial network corresponding to that time series included both prefrontal and temporal-parietal regions as well as insula activity, which previously have been related schizotypy and the development of schizophrenia.

KW - archetypical analysis

KW - decomposition

KW - functional connectivity

KW - social anhedonia

KW - support vector classification

UR - http://www.scopus.com/inward/record.url?scp=85070672968&partnerID=8YFLogxK

U2 - 10.1002/hbm.24751

DO - 10.1002/hbm.24751

M3 - Journal article

VL - 40

SP - 4965

EP - 4981

JO - Human Brain Mapping

JF - Human Brain Mapping

SN - 1065-9471

IS - 17

ER -

ID: 57797888